Interplay between personality traits and learning strategies: the missing link.

Students with varying personality traits are likely to employ diverse learning and study strategies. However, this relationship has never been explored in the medical education context. This study's aim was to explore the relationship between learning strategies and personality traits among medical students. This study was a cross-sectional study, and a quantitative approach was employed using two self-administered questionnaires: one to assess the personality traits from the Five-Factor Model (Conscientiousness, Neuroticism, Extraversion, Openness, and Agreeableness), and the other to assess 10 learning strategies (Anxiety, Attitude, Concentration, Information Processing, Motivation, Selecting Main Ideas, Self-Testing, Test Strategies, Time Management, and Using Academic Resources). A stratified random sampling technique was used to recruit medical students at Alfaisal University in the preclinical and clinical years (N = 309). Pearson correlation coefficient was used to measure the relationship between variables, and linear regression was used to evaluate how personality traits predicted learning strategy selection. Personality traits predicted the selection of learning strategies, especially Conscientiousness and Neuroticism. Conscientiousness showed a positive correlation with seven learning strategies and was the most important predictor of learning strategies students employ. Neuroticism correlations and predictions were negative. The other three traits showed weaker correlations. These correlations were between Extraversion and Using Academic Resources (r = 0.27), Information Processing (r = 0.23), and Attitude (r = 0.19); Openness and Information Processing (r = 0.29); and Agreeableness and Attitude (r = 0.29). All personality domains influence at least one learning strategy, especially Conscientiousness and Neuroticism. This study helps build a foundation for individualized coaching and mentorship in medical education.NEW & NOTEWORTHY This study aspires to build a foundation for individualized coaching and mentorship in medical education through utilizing personality traits to empower academic success. We demonstrate that all personality domains influence students' selection of at least one learning strategy, especially Conscientiousness and Neuroticism.

Motor imagery based brain computer interface using transform domain features.

Brain Computer Interface (BCI) is a channel of communication between the human brain and an external device through brain electrical activity. In this paper, we extracted different features to boost the classification accuracy as well as the mutual information of BCI systems. The extracted features include the magnitude of the discrete Fourier transform and the wavelet coefficients for the EEG signals in addition to distance series values and invariant moments calculated for the reconstructed phase space of the EEG measurements. Different preprocessing, feature selection, and classification schemes were utilized to evaluate the performance of the proposed system for dataset III from BCI competition II. The maximum accuracy achieved was 90.7% while the maximum mutual information was 0.76 bit obtained using the distance series features.